Tracking of objects is important for many computer vision applications. This is a relatively easy task when the objects are isolated and easily distinguished from the background. However, in complex and crowded environments, many objects are present that may have similar appearances, and occlude one another; also occlusions by other scene objects are common.
Traditional feature-based tracking methods, such as those based on color, salient points, or motion, do not have a discriminative model that distinguishes the object category of interest from others. Use of object detectors as discriminative models may help overcome this limitation. The accuracy of the state-of-the-art object detectors is still far from perfect. Missed detections, false alarms and inaccurate responses are common. Such tracking methods therefore are forced to function with such failures, and also with the difficulties due to occlusions and appearance similarity among multiple objects.